Open Source is much more than "just code". So much more that our guest John Mertic actually wrote a book to tell you all about it! As we are always happy to invite John on to the podcast, we invited him to talk about the book and, as usual, much more. So much more that this inevitably became a multi-part episode and in this part we are talking mostly about getting ready for Open Source.Open Source Projects- Beyond CodeGet John's new book on Amazon.This was not a sponsored episode. We just really liked the book and always love to talk with John. So we did. :)Also, we are not affiliated with Amazon so we're not getting any money when you make a purchase!Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Open Source is much more than "just code". So much more that our guest John Mertic actually wrote a book to tell you all about it! As we are always happy to invite John on to the podcast, we invited him to talk about the book and, as usual, much more. So much more that this inevitably became a multi-part episode and in this part we are talking mostly about getting ready for Open Source. Open Source Projects- Beyond Code Get John's new book on Amazon. This was not a sponsored episode. We just really liked the book and always love to talk with John. So we did. :) Also, we are not affiliated with Amazon so we're not getting any money when you make a purchase! Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 360 – Getting Ready for Open Source

Continuing the current series, here is the next value that we have picked up along the way that we find are important and lead to a healthy, productive, environment.Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Continuing the current series, here is the next value that we have picked up along the way that we find are important and lead to a healthy, productive, environment. Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 359 – Growth Mindset

Episode 358 – Help Each Other Thrive

Episode 357 – Progress Over Perfection

Episode 356 – High Say/Do Ratio

Continuing the current series, here are the next few values that we have picked up along the way that we find are important and lead to a healthy, productive, environment.Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Continuing the current series, here are the next few values that we have picked up along the way that we find are important and lead to a healthy, productive, environment. Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 355 – Our Roaring Values (p2)

After dissecting and commenting on other organizations' mission statements and values, we are now taking a look at our own house: what values have we picked up along the way that we find are important and lead to a healthy, productive, environment?Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

After dissecting and commenting on other organizations' mission statements and values, we are now taking a look at our own house: what values have we picked up along the way that we find are important and lead to a healthy, productive, environment? Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 354 – Our Roaring Values

Companies have, or pretend to have, values and those are usually nicely encapsulate in a mission statements. But how accurate are these and do they really represent the company fairly? In this and the next few episodes, we are going to have a little bit of fun discussing various well known mission statements and along the way give our personal opinions on them. Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Companies have, or pretend to have, values and those are usually nicely encapsulate in a mission statements. But how accurate are these and do they really represent the company fairly? In this and the next few episodes, we are going to have a little bit of fun discussing various well known mission statements and along the way give our personal opinions on them. Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 353 – Company  Hit & Miss-ion  Statements? (p5)

Episode 352 – Company  Hit & Miss-ion  Statements? (p4)

Episode 351 – Company  Hit & Miss-ion  Statements? (p3)

I look forward to every episode. Keep it up!

Great to listen

Great job!

Roaring Elephant

Alex Zeltov

In this episode, Global Black Belt and Technical Architect in Big Data and Advanced Analytics Team at Microsoft, Alex Zeltov, is our guest and he explains the in's and out's of MLOps though various tools like mlflow and kubeflowIn this first episode, Alex talks on a more theoretical level about MLOps and the benefits it can deliver.For more from Alex on MLOps and mlflow, check out his presentation at the Washington DC DataWorks Summit a couple, of weeks ago. The slides are now available on SlideShare and the video is available on YouTube:https://www.youtube.com/watch?v=Ns82mJjJgtoMLOpsJust like DataOps follows on to DevOps, one may say that MLOps continues after DataOps. While there is a wikipedia page on the subject, there is not that much "prior art" available just yet.The main advantages that MLOps can deliver, according to Alex, are a much improved move to production of trained algorithmes, even allowing for CI/CD, and a more structured approach to training models where multiple data scientists can work together to achieve better results.mlflowOne of the main tools emerging at the moment is the DataBricks backed mlflow project. Though not an Apache project, it has been open sourced under the Apache License now and shows much promise.In the episode, Alex explains how mlflow integrates with your data science notebooks to allow for reliable  model management with minimal disruption.Kubeflow(Image taken from https://medium.com/@amina.alsherif/how-to-get-started-with-kubeflow-187792f3e99)The second contender to reach for the MLOps crown is Kubeflow.Even though less mature than mlflow, it is backed  by the very popular Kubernetes framework and that brings a large community together working on this project.Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

In this episode, Global Black Belt and Technical Architect in Big Data and Advanced Analytics Team at Microsoft, Alex Zeltov, is our guest and he explains the in's and out's of MLOps though various tools like mlflow and kubeflow In this first episode, Alex talks on a more theoretical level about MLOps and the benefits it can deliver. For more from Alex on MLOps and mlflow, check out his presentation at the Washington DC DataWorks Summit a couple, of weeks ago. The slides are now available on SlideShare and the video is available on YouTube: <a href="https://www.youtube.com/watch?v=Ns82mJjJgto" rel="nofollow" target="_blank">https://www.youtube.com/watch?v=Ns82mJjJgto</a> MLOps Just like DataOps follows on to DevOps, one may say that MLOps continues after DataOps. While there is a wikipedia page on the subject, there is not that much "prior art" available just yet. The main advantages that MLOps can deliver, according to Alex, are a much improved move to production of trained algorithmes, even allowing for CI/CD, and a more structured approach to training models where multiple data scientists can work together to achieve better results. mlflow One of the main tools emerging at the moment is the DataBricks backed mlflow project. Though not an Apache project, it has been open sourced under the Apache License now and shows much promise. In the episode, Alex explains how mlflow integrates with your data science notebooks to allow for reliable  model management with minimal disruption. Kubeflow (Image taken from <a href="https://medium.com/@amina.alsherif/how-to-get-started-with-kubeflow-187792f3e99" rel="nofollow" target="_blank">https://medium.com/@amina.alsherif/how-to-get-started-with-kubeflow-187792f3e99</a>) The second contender to reach for the MLOps crown is Kubeflow. Even though less mature than mlflow, it is backed  by the very popular Kubernetes framework and that brings a large community together working on this project. Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 145 – Alex Zeltov on MLOps with mlflow, kubeflow and other tools (part 1)

Data Lifecycle

Does the standard Dev-Test-Prod cycle make sense in a Big Data environment or should you approach this subject a little differently?In this episode, we sum up our experiences and best practice tips regarding the infrastructure part and Data Lifecycle  will be features in the next topic episode.Planning on attending the Melbourne @DataWorksSummit? Send email to DWS18APAC@roaringelephant.org for a free ticket to the Melbourne event in February! Big thanks to @DataWorksSummit &amp; @hortonworks for sponsoring this giveaway!Dataworks Summit Barcelona is also rapidly approaching. You can find my dynamic sessions statistics dashboard here: https://aka.ms/DWS2019BAPlease use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Does the standard Dev-Test-Prod cycle make sense in a Big Data environment or should you approach this subject a little differently? In this episode, we sum up our experiences and best practice tips regarding the infrastructure part and Data Lifecycle  will be features in the next topic episode. Planning on attending the Melbourne @DataWorksSummit? Send email to DWS18APAC@roaringelephant.org for a free ticket to the Melbourne event in February! Big thanks to @DataWorksSummit &amp; @hortonworks for sponsoring this giveaway! Dataworks Summit Barcelona is also rapidly approaching. You can find my dynamic sessions statistics dashboard here: <a href="https://aka.ms/DWS2019BA" rel="nofollow" target="_blank">https://aka.ms/DWS2019BA</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 121 – Infrastructure and Data Lifecycle (part 1)

Matteo and Sijie from Streamlio reached out to us and let us know they had an update on Apache Pulsar. It turned out they had a lot to talk about so we cut the interview in two parts and here is the first part where they introduce Apache Pulsar, go in depth on the correct deployment scaling of a stable Pulsar cluster and clarify Pulsars "at least once vs exactly once" strategy. Part two will go in more depth on what's new. Stay tuned!Apache Pulsar logoMatteo Merli (https://www.linkedin.com/in/matteomerli/) 	Co-Founder - Software EngineerSijie Guo (https://www.linkedin.com/in/samuelguo/) 	Co-FounderApache Pulsar (incubating)https://pulsar.apache.org/Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Matteo and Sijie from Streamlio reached out to us and let us know they had an update on Apache Pulsar. It turned out they had a lot to talk about so we cut the interview in two parts and here is the first part where they introduce Apache Pulsar, go in depth on the correct deployment scaling of a stable Pulsar cluster and clarify Pulsars "at least once vs exactly once" strategy. Part two will go in more depth on what's new. Stay tuned! Apache Pulsar logo Matteo Merli (<a href="https://www.linkedin.com/in/matteomerli/" rel="nofollow" target="_blank">https://www.linkedin.com/in/matteomerli/</a>) 	Co-Founder - Software Engineer Sijie Guo (<a href="https://www.linkedin.com/in/samuelguo/" rel="nofollow" target="_blank">https://www.linkedin.com/in/samuelguo/</a>) 	Co-Founder Apache Pulsar (incubating) <a href="https://pulsar.apache.org/" rel="nofollow" target="_blank">https://pulsar.apache.org/</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 101 – Apache Pulsar update with Matteo and Sijie from Streamlio

It's another Roaring News episode. Today Jhon talks about machine learning projects for beginners, data visualization and the new neural network hotness which is transfer learning. Dave covers the Dataworks Summit call for papers and Apache Impala reaching Top Level Project status.Breaking NewsJhon 	8 Fun Machine Learning Projects for Beginners 	https://elitedatascience.com/machine-learning-projects-for-beginnersData is Beautiful 	https://www.reddit.com/r/dataisbeautiful/ https://twitter.com/hashtag/dataisbeautifulTransfer Learning - Machine Learning's Next Frontier 	http://ruder.io/transfer-learning/index.html#whatistransferlearningDave 	Apache Impala gains TLP 	https://blogs.apache.org/foundation/entry/the-apache-software-foundation-announces24Dataworks Summit call for papers 	https://dataworkssummit.com/blog/dataworks-summit-berlin-call-for-papers-is-now-open/Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

It's another Roaring News episode. Today Jhon talks about machine learning projects for beginners, data visualization and the new neural network hotness which is transfer learning. Dave covers the Dataworks Summit call for papers and Apache Impala reaching Top Level Project status. Breaking News Jhon 	8 Fun Machine Learning Projects for Beginners 	<a href="https://elitedatascience.com/machine-learning-projects-for-beginners" rel="nofollow" target="_blank">https://elitedatascience.com/machine-learning-projects-for-beginners</a> Data is Beautiful 	<a href="https://www.reddit.com/r/dataisbeautiful/" rel="nofollow" target="_blank">https://www.reddit.com/r/dataisbeautiful/</a> <a href="https://twitter.com/hashtag/dataisbeautiful" rel="nofollow" target="_blank">https://twitter.com/hashtag/dataisbeautiful</a> Transfer Learning - Machine Learning's Next Frontier 	<a href="http://ruder.io/transfer-learning/index.html#whatistransferlearning" rel="nofollow" target="_blank">http://ruder.io/transfer-learning/index.html#whatistransferlearning</a> Dave 	Apache Impala gains TLP 	<a href="https://blogs.apache.org/foundation/entry/the-apache-software-foundation-announces24" rel="nofollow" target="_blank">https://blogs.apache.org/foundation/entry/the-apache-software-foundation-announces24</a> Dataworks Summit call for papers 	<a href="https://dataworkssummit.com/blog/dataworks-summit-berlin-call-for-papers-is-now-open/" rel="nofollow" target="_blank">https://dataworkssummit.com/blog/dataworks-summit-berlin-call-for-papers-is-now-open/</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 65 – Roaring news

For a podcast on Big Data, we were amazed that we never covered the subject of NoSQL. So we're correcting this today. Not by listing and comparing all the NoSQL solutions out there, but rather by going over the differences between the two paradigms. This way we hope to offer enough insight so you can feel comfortable deciding if you should or should not deploy NoSQL in your environment.There are definitely a number of really important benefits to using a NoSQL solution in your environment and your co-hosts are big fans of the technology. However, make sure to carefully consider the positive and negative consequences. Make sure you are going for NoSQL for the right reasons.The discussion in this episode should give you a good bases for that decision but do let us know if there is something we missed?Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

For a podcast on Big Data, we were amazed that we never covered the subject of NoSQL. So we're correcting this today. Not by listing and comparing all the NoSQL solutions out there, but rather by going over the differences between the two paradigms. This way we hope to offer enough insight so you can feel comfortable deciding if you should or should not deploy NoSQL in your environment. There are definitely a number of really important benefits to using a NoSQL solution in your environment and your co-hosts are big fans of the technology. However, make sure to carefully consider the positive and negative consequences. Make sure you are going for NoSQL for the right reasons. The discussion in this episode should give you a good bases for that decision but do let us know if there is something we missed? Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 155 – NoSQL: You keep using that word…

In this episode, Global Black Belt and Technical Architect in Big Data and Advanced Analytics Team at Microsoft, Alex Zeltov, is our guest and he explains the in's and out's of MLOps though various tools like mlflow and kubeflowIn this second part, we go into more depth on the practical consequences of implementing MLOps and the various tools that are available.We also go on a bit of a tangent discussing why traditional enterprises are still having a hard time to look at machine learning models as something that requires and benefits from things like model management, version control and periodic updating of models.For more from Alex on MLOps and mlflow, check out his presentation at the Washington DC DataWorks Summit a couple, of weeks ago. The slides are now available on SlideShare and the video is available on YouTube:https://www.youtube.com/watch?v=Ns82mJjJgtoMLOpsJust like DataOps follows on to DevOps, one may say that MLOps continues after DataOps. While there is a wikipedia page on the subject, there is not that much "prior art" available just yet.The main advantages that MLOps can deliver, according to Alex, are a much improved move to production of trained algorithmes, even allowing for CI/CD, and a more structured approach to training models where multiple data scientists can work together to achieve better results.mlflowOne of the main tools emerging at the moment is the DataBricks backed mlflow project. Though not an Apache project, it has been open sourced under the Apache License now and shows much promise.In the episode, Alex explains how mlflow integrates with your data science notebooks to allow for reliable  model management with minimal disruption.Kubeflow(Image taken from https://medium.com/@amina.alsherif/how-to-get-started-with-kubeflow-187792f3e99)The second contender to reach for the MLOps crown is Kubeflow.Even though less mature than mlflow, it is backed  by the very popular Kubernetes framework and that brings a large community together working on this project.Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

In this episode, Global Black Belt and Technical Architect in Big Data and Advanced Analytics Team at Microsoft, Alex Zeltov, is our guest and he explains the in's and out's of MLOps though various tools like mlflow and kubeflow In this second part, we go into more depth on the practical consequences of implementing MLOps and the various tools that are available. We also go on a bit of a tangent discussing why traditional enterprises are still having a hard time to look at machine learning models as something that requires and benefits from things like model management, version control and periodic updating of models. For more from Alex on MLOps and mlflow, check out his presentation at the Washington DC DataWorks Summit a couple, of weeks ago. The slides are now available on SlideShare and the video is available on YouTube: <a href="https://www.youtube.com/watch?v=Ns82mJjJgto" rel="nofollow" target="_blank">https://www.youtube.com/watch?v=Ns82mJjJgto</a> MLOps Just like DataOps follows on to DevOps, one may say that MLOps continues after DataOps. While there is a wikipedia page on the subject, there is not that much "prior art" available just yet. The main advantages that MLOps can deliver, according to Alex, are a much improved move to production of trained algorithmes, even allowing for CI/CD, and a more structured approach to training models where multiple data scientists can work together to achieve better results. mlflow One of the main tools emerging at the moment is the DataBricks backed mlflow project. Though not an Apache project, it has been open sourced under the Apache License now and shows much promise. In the episode, Alex explains how mlflow integrates with your data science notebooks to allow for reliable  model management with minimal disruption. Kubeflow (Image taken from <a href="https://medium.com/@amina.alsherif/how-to-get-started-with-kubeflow-187792f3e99" rel="nofollow" target="_blank">https://medium.com/@amina.alsherif/how-to-get-started-with-kubeflow-187792f3e99</a>) The second contender to reach for the MLOps crown is Kubeflow. Even though less mature than mlflow, it is backed  by the very popular Kubernetes framework and that brings a large community together working on this project. Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 147 – Alex Zeltov on MLOps with mlflow, kubeflow and other tools (part 2)

This episode, a group of people from Esgyn join us to talk about the Apache Trafodion transactional SQL for Hadoop database engine.In this second part Rohit, Ken and Rao talk about the internal workings and best practices of Apache Trafodion.Rohit JainChief Technology Officer (linkedin)https://esgyn.comKen HoltChief Operating Officer and Co-Founder (linkedin)https://esgyn.comRao KakarlamudiVP of Pre-sales &amp; Principal Architect (linkedin)https://esgyn.comIn Search of Database Nirvana (oreilly)By Rohit JainPlease use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

This episode, a group of people from Esgyn join us to talk about the Apache Trafodion transactional SQL for Hadoop database engine. In this second part Rohit, Ken and Rao talk about the internal workings and best practices of Apache Trafodion. Rohit Jain Chief Technology Officer (linkedin) <a href="https://esgyn.com" rel="nofollow" target="_blank">https://esgyn.com</a> Ken Holt Chief Operating Officer and Co-Founder (linkedin) <a href="https://esgyn.com" rel="nofollow" target="_blank">https://esgyn.com</a> Rao Kakarlamudi VP of Pre-sales &amp; Principal Architect (linkedin) <a href="https://esgyn.com" rel="nofollow" target="_blank">https://esgyn.com</a> In Search of Database Nirvana (oreilly) By Rohit Jain Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 78 – Apache Trafodion transactional SQL for Hadoop (Part 2)

Mandy Chessell

In this GDPR world, Data Governance and Data Lineage are, or should be, very much top of mind for anybody in the Big Data world. We reached out to Mandy Chessell, who has been very active in this area and were delighted when she accepted to do an interview with us.In this first part, the focus is more on Mandy herself and we lay the groundwork for the second part that will go live in episode 109.Mandy ChessellDistinguished Engineer, Master Inventor, Fellow of Royal Academy of Engineeringhttps://www.linkedin.com/in/mandy-chessell-a4989722/ODPi Blog post on Egeria: First Release of ODPi Egeria is Here 	ODPi github projects: 	Egeria - Open Metadata and Governance 	https://github.com/odpi/egeriaData-governance companion project 	https://github.com/odpi/data-governancePlease use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

In this GDPR world, Data Governance and Data Lineage are, or should be, very much top of mind for anybody in the Big Data world. We reached out to Mandy Chessell, who has been very active in this area and were delighted when she accepted to do an interview with us. In this first part, the focus is more on Mandy herself and we lay the groundwork for the second part that will go live in episode 109. Mandy Chessell Distinguished Engineer, Master Inventor, Fellow of Royal Academy of Engineering <a href="https://www.linkedin.com/in/mandy-chessell-a4989722/" rel="nofollow" target="_blank">https://www.linkedin.com/in/mandy-chessell-a4989722/</a> ODPi Blog post on Egeria: First Release of ODPi Egeria is Here 	ODPi github projects: 	Egeria - Open Metadata and Governance 	<a href="https://github.com/odpi/egeria" rel="nofollow" target="_blank">https://github.com/odpi/egeria</a> Data-governance companion project 	<a href="https://github.com/odpi/data-governance" rel="nofollow" target="_blank">https://github.com/odpi/data-governance</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 107 – Open Metadata and Governance Masterclass with Mandy Chessell – Part 1

H2O.ai

We recently sat down with Kuba and Pavel from H2O to discuss how you can easily lift your Spark notebooks to the next level by adding some H20 to it using their open source Sparkling Water project.In this first part of the interview, we cover the conceptual principles behind Sparkling water and discuss some existing use case implementations.Jakub "Kuba" HavaSenior Software Engineer at H2O.aiPavel PscheidlMachine learning engineer at H2O.ai, Software engineer, WriterH2O World San FransiscoFind out more at the upcoming H2O World conference in San Fransisco on February 4-5, 2019Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

We recently sat down with Kuba and Pavel from H2O to discuss how you can easily lift your Spark notebooks to the next level by adding some H20 to it using their open source Sparkling Water project. In this first part of the interview, we cover the conceptual principles behind Sparkling water and discuss some existing use case implementations. Jakub "Kuba" Hava Senior Software Engineer at <a href="http://H2O.ai" rel="nofollow" target="_blank">H2O.ai</a> Pavel Pscheidl Machine learning engineer at <a href="http://H2O.ai" rel="nofollow" target="_blank">H2O.ai</a>, Software engineer, Writer H2O World San Fransisco Find out more at the upcoming H2O World conference in San Fransisco on February 4-5, 2019 Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 125 – Sparkling Water with H2O.AI (Part 1)

A little while ago we came across a blog by Martin Zinkevich about the best practices for ML Engineering at Google. We had this one on the back burner for a while now and we decided to go ahead and do an episode on this subject, using this article as a rough guide.Rules of Machine Learning:Best Practices for ML EngineeringThe blog post by Martin is definitely worth a thorough read!https://developers.google.com/machine-learning/guides/rules-of-ml/Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

A little while ago we came across a blog by Martin Zinkevich about the best practices for ML Engineering at Google. We had this one on the back burner for a while now and we decided to go ahead and do an episode on this subject, using this article as a rough guide. Rules of Machine Learning:Best Practices for ML Engineering The blog post by Martin is definitely worth a thorough read! <a href="https://developers.google.com/machine-learning/guides/rules-of-ml/" rel="nofollow" target="_blank">https://developers.google.com/machine-learning/guides/rules-of-ml/</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 165 – Best Practices for Machine Learning

This is the second part of an interview with Fangjin Yang, co-founder and CEO at Imply and committer/PMC member for the Druid project. Druid: a high-performance, column-oriented, distributed data store which has entered the Hadoop environment with the recent integration with Apache and we since Druid has been around for a while, we are grateful to FJ for spending some time with our listeners.Fangjin YangCofounder and CEO at Imply (linkedin)Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

This is the second part of an interview with Fangjin Yang, co-founder and CEO at Imply and committer/PMC member for the Druid project. Druid: a high-performance, column-oriented, distributed data store which has entered the Hadoop environment with the recent integration with Apache and we since Druid has been around for a while, we are grateful to FJ for spending some time with our listeners. Fangjin Yang Cofounder and CEO at Imply (linkedin) Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 87 – Druid: a high-performance, column-oriented, distributed data store – part 2

As promised, in this final part of our Hadoop Sizing series, we round off the subject with sizing your compute and network resources. Undoubtedly we'll be revisiting this subject in the future, but the three parts of this series should give ample information on the subject for now.Hadoop Node SizingHadoop Data Node Density Tradeoff  on HCC: https://community.hortonworks.com/content/kbentry/48878/hadoop-data-node-density-tradeoff.htmlPlease use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

As promised, in this final part of our Hadoop Sizing series, we round off the subject with sizing your compute and network resources. Undoubtedly we'll be revisiting this subject in the future, but the three parts of this series should give ample information on the subject for now. Hadoop Node Sizing Hadoop Data Node Density Tradeoff  on HCC: <a href="https://community.hortonworks.com/content/kbentry/48878/hadoop-data-node-density-tradeoff.html" rel="nofollow" target="_blank">https://community.hortonworks.com/content/kbentry/48878/hadoop-data-node-density-tradeoff.html</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 74 – Hadoop sizing part 3: Compute sizing

Welcome to 2018! And welcome to our 110% fact based prediction show for 2018. As you may expect from your two hosts, everything in this episode is 110% sure to become reality in the next twelve months. And since 110% is not actually possible, our predictions might also be just a little bit off? But we have 365 days to bask in the glory of our predictions before we, as usual, are shot back down to earth.Nancy comicDave 	The year of cloud first and hybrid cloud 	Many organisations will move from solely on prem to cloud or hybrid with new workloads seeking alternatives to their traditional on-prem.Edge computing for IoT 	With edge devices becoming more powerful and IoT workloads increasing (and bandwidth not getting cheaper at the same rate) we'll see more of the intelligence pushed further to the edge.GDPR will fundamentally change the face of data governance, collection, anon, retention in big data 	GDPR regulations start to arrive in the form of concrete plans being in place and many organisations will start to feel the governance pressure especially in their data lake(s).Big Data Cybersecurity adoption significantly increased 	A lot of work has gone into preparation, which I believe we'll start to see realised in 2018, not commonplace yet, but significantly increased deployments over 2017.AI and ML will replace "Big Data" in general parlance 	The phrase "big data" will become secondary and the focus will be on Artificial Intelligence and Machine Learning, regardless of the underlying big data platform.Jhon 	Cloud will become the de-facto choice for big data deployment. 	The decoupled data storage and ephemeral compute make it possible for small entities to go for it AND makes it possible for small companies to create scalable solutions for the big guys.In house development is too slow since good, reusable solutions become available cross industry. 	Because of cloud, we'll see a shift where big data solutions are no longer built in-house, but "ready" solutions are licensed/purchased from start-up like orgs. (Self service Big Data as a Service made possible by cloud.) Hadoop becomes invisible, a bit like programing frameworks.Data Science and Engineer jobs will change 	Data Scientists domain knowledge becomes the main advantage and "math" becomes the base knowledge. 	Data engineer will change a lot due to AI taking over a lot of the data prep work. May even disappear or go back to simple data ingest.Still no good governance solution in 2018 	Atlas will get better, with IBM behind it, but will still fail to offer the comprehensive governance and lineage solution businesses are looking for. And sadly, no other solution will fit the bill either.Chat bots (in social media) are here to stay. 	Example Blue bot: https://tweakers.net/nieuws/133099/klm-laat-bots-antwoorden-formuleren-op-helft-van-klantvragen-op-social-media.htmlPlease use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Welcome to 2018! And welcome to our 110% fact based prediction show for 2018. As you may expect from your two hosts, everything in this episode is 110% sure to become reality in the next twelve months.   And since 110% is not actually possible, our predictions might also be just a little bit off? But we have 365 days to bask in the glory of our predictions before we, as usual, are shot back down to earth. Nancy comic Dave 	The year of cloud first and hybrid cloud 	Many organisations will move from solely on prem to cloud or hybrid with new workloads seeking alternatives to their traditional on-prem. Edge computing for IoT 	With edge devices becoming more powerful and IoT workloads increasing (and bandwidth not getting cheaper at the same rate) we'll see more of the intelligence pushed further to the edge. GDPR will fundamentally change the face of data governance, collection, anon, retention in big data 	GDPR regulations start to arrive in the form of concrete plans being in place and many organisations will start to feel the governance pressure especially in their data lake(s). Big Data Cybersecurity adoption significantly increased 	A lot of work has gone into preparation, which I believe we'll start to see realised in 2018, not commonplace yet, but significantly increased deployments over 2017. AI and ML will replace "Big Data" in general parlance 	The phrase "big data" will become secondary and the focus will be on Artificial Intelligence and Machine Learning, regardless of the underlying big data platform. Jhon 	Cloud will become the de-facto choice for big data deployment. 	The decoupled data storage and ephemeral compute make it possible for small entities to go for it AND makes it possible for small companies to create scalable solutions for the big guys. In house development is too slow since good, reusable solutions become available cross industry. 	Because of cloud, we'll see a shift where big data solutions are no longer built in-house, but "ready" solutions are licensed/purchased from start-up like orgs. (Self service Big Data as a Service made possible by cloud.) Hadoop becomes invisible, a bit like programing frameworks. Data Science and Engineer jobs will change 	Data Scientists domain knowledge becomes the main advantage and "math" becomes the base knowledge. 	Data engineer will change a lot due to AI taking over a lot of the data prep work. May even disappear or go back to simple data ingest. Still no good governance solution in 2018 	Atlas will get better, with IBM behind it, but will still fail to offer the comprehensive governance and lineage solution businesses are looking for. And sadly, no other solution will fit the bill either. Chat bots (in social media) are here to stay. 	Example Blue bot: <a href="https://tweakers.net/nieuws/133099/klm-laat-bots-antwoorden-formuleren-op-helft-van-klantvragen-op-social-media.html" rel="nofollow" target="_blank">https://tweakers.net/nieuws/133099/klm-laat-bots-antwoorden-formuleren-op-helft-van-klantvragen-op-social-media.html</a> Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 68 – Future Predictions

Chris Bergh

DataKitchen.io's Chris Bergh takes us down the path towards successful DataOps implementation.If you have not heard of the DataOps concept yet and data is a big part of your environment (and really, it should be) we're sure you will find more than a couple takeaways here!Christopher Bergh (@ChrisBergh)CEO &amp; Head Chef, DataKitchenThe DataOps Cookbook 	DataOps is NOT Just DevOps for DataPlease use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

<a href="http://DataKitchen.io" rel="nofollow" target="_blank">DataKitchen.io</a>'s Chris Bergh takes us down the path towards successful DataOps implementation. If you have not heard of the DataOps concept yet and data is a big part of your environment (and really, it should be) we're sure you will find more than a couple takeaways here! Christopher Bergh (@ChrisBergh) CEO &amp; Head Chef, DataKitchen The DataOps Cookbook 	DataOps is NOT Just DevOps for Data Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

Episode 137 – Interview on DataOps with Chris Bergh of DataKitchen.io (Part 1)

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